In [39]:
import numpy as np
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
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!display?
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!display
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!display
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from bokeh.io import output_notebook, output_file
from bokeh.plotting import show, figure
from bokeh.layouts import gridplot, column
from bokeh.models import HoverTool, ColumnDataSource, Jitter, Label, Span, LinearInterpolator, CategoricalColorMapper
from bokeh.models.widgets import Panel, Tabs, Div
from datetime import datetime as dt
import time
import numpy as np
import pandas as pd
from pandas.io import gbq
projectId = 'mindful-carport-156107'
output_notebook()
h = 400
w = 1200
bbSmall = 5
bbBig = 50
legendCat1 = '6pt'
legendCity = '6pt'
legendSource = '8pt'
startDate = '2017-03-01'
tvcDate1 = time.mktime(dt(2017, 5, 12, 2, 0, 0).timetuple())*1000
tvcDate = time.mktime(dt(2017, 9, 21, 2, 0, 0).timetuple())*1000
tvcDate2 = time.mktime(dt(2017, 11, 13, 2, 0, 0).timetuple())*1000
eurekaDate = time.mktime(dt(2017, 10, 6, 2, 0, 0).timetuple())*1000
hash = {}
c = ('aliceblue', 'antiquewhite', 'aqua', 'aquamarine', 'azure', 'beige', 'bisque', 'black',
'blanchedalmond', 'blue', 'blueviolet', 'brown', 'burlywood', 'cadetblue', 'chartreuse',
'chocolate', 'coral', 'cornflowerblue', 'cornsilk', 'crimson', 'cyan', 'darkblue', 'darkcyan',
'darkgoldenrod', 'darkgray', 'darkgreen', 'darkgrey', 'darkkhaki', 'darkmagenta', 'darkolivegreen',
'darkorange', 'darkorchid', 'darkred', 'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray',
'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'deepskyblue', 'dimgray', 'dimgrey', 'dodgerblue',
'firebrick', 'floralwhite', 'forestgreen', 'fuchsia', 'gainsboro', 'ghostwhite', 'gold', 'goldenrod',
'gray', 'green', 'greenyellow', 'grey', 'honeydew', 'hotpink', 'indianred', 'indigo', 'ivory', 'khaki',
'lavender', 'lavenderblush', 'lawngreen', 'lemonchiffon', 'lightblue', 'lightcoral', 'lightcyan',
'lightgoldenrodyellow', 'lightgray', 'lightgreen', 'lightgrey', 'lightpink', 'lightsalmon', 'lightseagreen',
'lightskyblue', 'lightslategray', 'lightslategrey', 'lightsteelblue', 'lightyellow', 'lime', 'limegreen',
'linen', 'magenta', 'maroon', 'mediumaquamarine', 'mediumblue', 'mediumorchid', 'mediumpurple', 'mediumseagreen',
'mediumslateblue', 'mediumspringgreen', 'mediumturquoise', 'mediumvioletred', 'midnightblue', 'mintcream', 'mistyrose',
'moccasin', 'navajowhite', 'navy', 'oldlace', 'olive', 'olivedrab', 'orange', 'orangered', 'orchid', 'palegoldenrod',
'palegreen', 'paleturquoise', 'palevioletred', 'papayawhip', 'peachpuff', 'peru', 'pink', 'plum', 'powderblue',
'purple', 'red', 'rosybrown', 'royalblue', 'saddlebrown', 'salmon', 'sandybrown', 'seagreen', 'seashell', 'sienna',
'silver', 'skyblue', 'slateblue', 'slategray', 'slategrey', 'snow', 'springgreen', 'steelblue', 'tan', 'teal',
'thistle', 'tomato', 'turquoise', 'violet', 'wheat', 'white', 'whitesmoke', 'yellow', 'yellowgreen');
# Holoviews plot
import holoviews as hv
from pandas.io import gbq
hv.extension('bokeh')
h = 600
w = 1500
from plotly import tools
import plotly.graph_objs as go
import plotly as py
from plotly.graph_objs import Scatter, Layout
py.offline.init_notebook_mode()
import cufflinks as cf
cf.set_config_file(offline=True, world_readable=False, theme='pearl')
import plotly.figure_factory as ff
Analyze¶
In [43]:
#Importing the libraries
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt # basic plotting
import seaborn as sns # more plotting
#Importing the data
events = pd.read_csv('./football-events/events.csv')
ginf = pd.read_csv('./football-events/ginf.csv')
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display( ginf.head(10))
ginf.info()
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# display( events.head())
# events.info()
# display(events[['event_team','opponent']].describe())
# events[['event_team','opponent']].iplot(kind='histogram', subplots=True)
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# df = ginf.copy().groupby(['date','country']).agg(['count']).reset_index()
df = ginf.copy().groupby(['date','country']).size().reset_index(name='counts')
df = pd.pivot_table(df, index=['date'], columns=['country'], values='counts', aggfunc=np.sum)
df = df.fillna(0)
df.dtypes
# df.date = pd.to_datetime(df.date)
df.index = pd.to_datetime(df.index)
df['total'] = df['england']+df['france']+df['germany']+df['italy']+df['spain']
df.resample('W').sum().iplot(kind='scatter', subplots=True, shape=(6,1), shared_xaxes=True, subplot_titles=True)
In [47]:
!jupyter nbconvert --to html --CodeFoldingPreprocessor.remove_folded_code=True football.ipynb
from shutil import copy2
copy2('football.html','C:/Users/Khanh/Google Drive/Github/khanhdinh.github.io/_posts/2017-12-09-football.html')
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!nbconvert?
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!jupyter --path